Fixes

    # If 3D and last dimension isn't 1, assume this is one-hot encoded and return as-is.

    if y.shape[-1] != 1:

      return y

      #raise ValueError(

      #    "y must be a float scalar or a ndarray of shape `(N,)` or `(N, n_tasks)` or `(N, n_tasks, 1)`."

      #)

    y_out = np.squeeze(y, axis=-1)

  # Handle classification. We need to convert labels into one-hot

  # representation.

y = np.random.randint(3, size=(N,))

dataset = dc.data.NumpyDataset(X, y)

sklearn_model = RandomForestClassifier(

    class_weight="balanced", n_estimators=50)

sklearn_model = RandomForestClassifier(class_weight="balanced", n_estimators=50)

model = dc.models.SklearnModel(sklearn_model)

# Fit trained model

model.save()

print("About to evaluate model")

train_scores = model.evaluate(dataset,

    sklearn.metrics.roc_auc_score, [])

train_scores = model.evaluate(dataset, sklearn.metrics.roc_auc_score, [])

print("Train scores")

print(train_scores)